Accessing Cryptosporidium Omic and Isolate Data via CryptoDB.org

Methods Mol Biol. 2020;2052:139-192. doi: 10.1007/978-1-4939-9748-0_10.

Abstract

Cryptosporidium has historically been a difficult organism to work with, and molecular genomic data for this important pathogen have typically lagged behind other prominent protist pathogens. CryptoDB ( http://cryptodb.org/ ) was launched in 2004 following the appearance of draft genome sequences for both C. parvum and C. hominis. CryptoDB merged with the EuPathDB Bioinformatics Resource Center family of databases ( https://eupathdb.org ) and has been maintained and updated regularly since its establishment. These resources are freely available, are web-based, and permit users to analyze their own sequence data in the context of reference genome sequences in our user workspaces. Advances in technology have greatly facilitated Cryptosporidium research in the last several years greatly enhancing and extending the data and types of data available for this genus. Currently, 13 genome sequences are available for 9 species of Cryptosporidium as well as the distantly related Gregarina niphandrodes and two free-living alveolate outgroups of the Apicomplexa, Chromera velia and Vitrella brassicaformis. Recent years have seen several new genome sequences for both existing and new Cryptosporidium species as well as transcriptomics, proteomics, SNP, and isolate population surveys. This chapter introduces the extensive data mining and visualization capabilities of the EuPathDB software platform and introduces the data types and tools that are currently available for Cryptosporidium. Key features are demonstrated with Cryptosporidium-relevant examples and explanations.

Keywords: Apicomplexa; Bioinformatics; Genomics; Orthology; Parasite; Pathogen; Proteomics; SNP; Sequence analysis; Transcriptomics.

MeSH terms

  • Computational Biology
  • Cryptosporidium / genetics*
  • Cryptosporidium parvum / genetics*
  • Data Mining
  • Databases, Genetic*
  • Gene Ontology
  • Genomics
  • Metabolic Networks and Pathways / genetics
  • Polymorphism, Single Nucleotide
  • Proteomics
  • Software
  • Whole Genome Sequencing
  • Workflow